Software Engineer, Workload Enablement
OpenAI · San Francisco, United States +1 · 6mo ago
About the Team
The Scaling team is responsible for the architectural and engineering backbone of OpenAI’s infrastructure. We design and deliver advanced systems that support the deployment and operation of cutting-edge AI models. Our work spans system software, networking, platform architecture, fleet-level monitoring, and performance optimization.
About the Role
We’re hiring an SW Engineer to enable production workloads and end-to-end testing on new platforms. This role will include creating new test harnesses and platform stress benchmarks, porting existing inference and training workloads to new, sometimes early-access, systems/hardware, analyzing performance and bottlenecks, and characterizing the end-to-end behavior of new systems (compute, comms, storage, control plane, and failure modes).
Key Responsibilities
Port and validate key inference and training workloads on new platforms/SKUs as they arrive; drive correctness, performance, and stability to an internal readiness bar.
Build a suite of benchmarks and stress tests that capture real E2E behavior of our workloads by exercising all aspects of a system, including CPU, GPU, memory subsystem, frontend, scale-up, and scale-out networking (including WAN traffic, NVlink and RDMA collectives), storage, thermals, and any other relevant parts.
Deep-dive performance on distributed training/inference:
Collective performance and tuning (across NCCL/RCCL and internal libraries)
Overlap of compute/communication, kernel-level bottlenecks, memory bandwidth and scheduling effects
Create repeatable test harnesses that run in CI / lab environments and produce actionable outputs (pass/fail, performance score, regression detection).
Partner with systems + fleet bring-up engineers to ensure the platform is not only stable and performant, but also operationally usable and scalable (containerization, K8s integration, telemetry hooks, failure triage loops).
Work cross-functionally with vendors and internal stakeholders by producing clear bug reports, minimal repros, and prioritized issue lists.
Qualifications
BS in CS/EE (or equivalent practical experience).
5+ years in one or more of: ML systems, performance engineering, distributed systems, or HPC.
Strong hands-on experience with:
PyTorch and modern LLM training/inference stacks
Large-scale distributed training concepts (data/model/pipeline parallel, collective comms)
Experience with RDMA and debugging/optimizing comms libraries (NCCL or RCCL) and their interaction with hardware/network
Proficiency in Python plus comfort reading/writing performance-critical code (C++/CUDA/HIP is a plus).
Strong profiling/debugging skills (e.g., Nsight, rocprof, perf, flamegraphs; ability to reason from traces/counters).
Preferred Skills
Experience building workload-shaped benchmarks and stress/fault tests that correlate to production behavior (not just synthetic loops or microbenchmarks).
Familiarity with RDMA networking and transport tuning; understanding of how network topology and congestion impact collectives.
Experience running and validating workloads in Kubernetes, and bridging “research code” into robust, repeatable infrastructure.
Hands-on lab experience with early hardware (new NICs, new GPUs/accelerators, early racks).
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
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